from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 	_UpperCamelCase (      snake_case__				)    -> None:
							__UpperCAmelCase     ,						__UpperCAmelCase    :      List[str]       = analyze_text(_lowerCamelCase				)
							__UpperCAmelCase    :      str       = list(" " + ascii_lowercase				)
							# what is our total sum of probabilities.
							__UpperCAmelCase    :      Optional[int]       = sum(single_char_strings.values()				)

							# one length string
							__UpperCAmelCase    :      int       = 0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					__UpperCAmelCase    :      Optional[Any]       = single_char_strings[ch]
																					__UpperCAmelCase    :      int       = my_str / all_sum
																					my_fir_sum += prob * math.loga(_lowerCamelCase				)  # entropy formula.

														# print entropy
							print(f'''{round(-1 * my_fir_sum				):.1f}'''				)

							# two len string
							__UpperCAmelCase    :      str       = sum(two_char_strings.values()				)
							__UpperCAmelCase    :      int       = 0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					__UpperCAmelCase    :      Any       = cha + cha
																					if sequence in two_char_strings:
																												__UpperCAmelCase    :      Union[str, Any]       = two_char_strings[sequence]
																												__UpperCAmelCase    :      Optional[int]       = int(_lowerCamelCase				) / all_sum
																												my_sec_sum += prob * math.loga(_lowerCamelCase				)

														# print second entropy
							print(f'''{round(-1 * my_sec_sum				):.1f}'''				)

							# print the difference between them
							print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}'''				)




def 	_UpperCamelCase (      snake_case__				)    -> tuple[dict, dict]:
							__UpperCAmelCase    :      Tuple       = Counter()  # type: ignore
							__UpperCAmelCase    :      Any       = Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0,     len(_lowerCamelCase				) - 1				):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def 	_UpperCamelCase (      )    -> Tuple:
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
	main()












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[str]          =		{}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							str          =		['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Any          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




